Experiment with Random Difficulty (results based on the bet)

## Nombre de participants se déclarant comme joueurs :  29

## Nombre de femmes se déclarant comme joueuses :  3

## Age médian des joueurs :  15

Removing Outliers based on BET

## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET SAVED SHEEPS: "

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET EXPLOIT DDA: vuq3c2tk6"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Total number of outliers:  5"

## [1] "Total number of outliers motor task:  2"

## [1] "Total number of outliers perceptive task:  1"

## [1] "Total number of outliers logical task:  2"

Removing Outliers based on CONFIDENCE SCALE

## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, a6dfu5ljd, dyg7cga2o, tmxmxmwhi, zp9bc59o5"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Total number of outliers:  5"

## [1] "Total number of outliers motor task:  0"

## [1] "Total number of outliers perceptive task:  5"

## [1] "Total number of outliers logical task:  0"

Modeling difficulties

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1953.7   1975.3   -972.8   1945.7     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1396 -0.7500  0.2888  0.7385  2.8481 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5631   0.7504  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0298     0.1873  -5.499 3.83e-08 ***
## difficulty    2.9618     0.2146  13.803  < 2e-16 ***
## timeNorm     -0.5280     0.2020  -2.614  0.00895 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.539       
## timeNorm   -0.571 -0.009

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0      1624         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.050110  
##  1st Qu.:-0.438217  
##  Median :-0.118832  
##  Mean   :-0.002364  
##  3rd Qu.: 0.296005  
##  Max.   : 1.658440  
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"

##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1261.1   1282.7   -626.5   1253.1     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3943 -0.3586  0.1131  0.3536  6.6338 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7241   0.8509  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.3288     0.2583 -12.885   <2e-16 ***
## difficulty    8.2778     0.4068  20.346   <2e-16 ***
## timeNorm     -0.2933     0.2674  -1.097    0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.650       
## timeNorm   -0.519 -0.046

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)

## The result is correct only if all data used by the model has not changed since model was fitted.

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)

## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0         0      1624 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.6765404  
##  1st Qu.:-0.4435738  
##  Median : 0.0778425  
##  Mean   :-0.0007671  
##  3rd Qu.: 0.4353921  
##  Max.   : 1.5192471  
## [1] "Intercept: -3.33 5.5e-38 ***"
## [1] "Difficulty: 8.28 5e-92 ***"
## [1] "Time: -0.293 0.27 :("
## [1] "R2 fixed: 0.34"
## [1] "R2 mixed: 0.44"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"

##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1426.5   1447.8   -709.2   1418.5     1504 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.9435 -0.5021 -0.1156  0.5089  4.9862 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.577    1.256   
## Number of obs: 1508, groups:  IDjoueur, 52
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.8650     0.2652  -7.033 2.01e-12 ***
## difficulty    5.6686     0.3206  17.680  < 2e-16 ***
## timeNorm     -1.9313     0.2573  -7.507 6.04e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.496       
## timeNorm   -0.378 -0.227

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##      1508         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.7902825  
##  1st Qu.:-0.7784485  
##  Median :-0.3355504  
##  Mean   :-0.0003123  
##  3rd Qu.: 0.7369882  
##  Max.   : 3.1275697  
## [1] "Intercept: -1.86 2e-12 ***"
## [1] "Difficulty: 5.67 6e-70 ***"
## [1] "Time: -1.93 6e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"

##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1442117

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.68759, p-value = 0.4917
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.07199342

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.30458, p-value = 0.7607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03301126

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08913015

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.48979, p-value = 0.6243
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.05061255

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.79975, p-value = 0.4239
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08596507

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3393258 
## 
## [1] "self.eff.on.level.s 0.34 0.013 *"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.51036, p-value = 0.6098
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07281435

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1554335

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.1214, p-value = 0.03389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2101231 
## 
## [1] "risk.av.on.level.s 0.21 0.034 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1347244

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09369113

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2162, p-value = 0.02668
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2137687 
## 
## [1] "age.on.level.s 0.21 0.027 *"

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2774, p-value = 0.2015
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1275074

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2377395 
## 
## [1] "sexe.on.level.m -0.24 0.032 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.077873, p-value = 0.9379
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008649769

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.26928, p-value = 0.7877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03108211

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.82775747 -0.05457213
## sample estimates:
## difference in location 
##             -0.4558716 
## 
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 347, p-value = 0.9453
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4361429  0.4780691
## sample estimates:
## difference in location 
##            -0.01100307

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 292, p-value = 0.7971
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8271570  0.5994594
## sample estimates:
## difference in location 
##            -0.04046848

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.51384, p-value = 0.6074
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03114828

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.5194, p-value = 0.0004325
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2108941 
## 
## [1] "pbg.on.error -0.21 0.00043 ***"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.4336, p-value = 0.1517
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07585348

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.74916, p-value = 0.4538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06883117

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.43819, p-value = 0.6613
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04025974

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.94693, p-value = 0.3437
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09049774

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.9311, p-value = 8.455e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.253602 
## 
## [1] "sexe.on.error 0.25 8.5e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9825, p-value = 0.04743
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2202014 
## 
## [1] "sexe.on.error.m 0.22 0.047 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3795, p-value = 0.01734
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2642985 
## 
## [1] "sexe.on.error.s 0.26 0.017 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4235, p-value = 0.01537
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.279739 
## 
## [1] "sexe.on.error.l 0.28 0.015 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4126, p-value = 8.517e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.05397826 0.12809100
## sample estimates:
## difference in location 
##             0.09264717 
## 
## [1] "sexe.on.error.2 0.093 8.5e-05 *** mean(A): -0.11 mean(B): -0.01"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 455, p-value = 0.04774
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.0005518641 0.1439064370
## sample estimates:
## difference in location 
##             0.07761885 
## 
## [1] "sexe.on.error.m.2 0.078 0.048 * mean(A): -0.097 mean(B): -0.012"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 489, p-value = 0.01678
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01781275 0.15896562
## sample estimates:
## difference in location 
##             0.09669579 
## 
## [1] "sexe.on.error.s.2 0.097 0.017 * mean(A): -0.11 mean(B): -0.004"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 432, p-value = 0.01476
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.02021314 0.16115025
## sample estimates:
## difference in location 
##              0.1018427 
## 
## [1] "sexe.on.error.l.2 0.1 0.015 * mean(A): -0.12 mean(B): -0.016"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.91097, p-value = 0.3623
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.05234983

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.21777, p-value = 0.8276
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02158799

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.15983, p-value = 0.873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01583119

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2413, p-value = 0.2145
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##     tau 
## 0.12803

Self efficacy and subjective difficulty error

## Warning: Removed 82 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.8685, p-value = 0.004124
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2249577 
## 
## [1] "self.eff.on.error -0.22 0.0041 **"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.686, p-value = 0.09179
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2294667 
## 
## [1] "self.eff.on.error -0.23 0.092 ."

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.3708, p-value = 0.1704
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1873078

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7973, p-value = 0.07228
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2564331 
## 
## [1] "self.eff.on.error -0.26 0.072 ."

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        0.00054 49     0.84 :(
##  2:      0.09375        0.02500 57     0.21 :(
##  3:      0.15625       -0.01300 57     0.33 :(
##  4:      0.21875        0.02400 57     0.46 :(
##  5:      0.28125       -0.01900 58     0.56 :(
##  6:      0.34375        0.00150 57     0.94 :(
##  7:      0.40625        0.00450 56     0.82 :(
##  8:      0.46875       -0.01600 58     0.65 :(
##  9:      0.53125        0.00450 56     0.84 :(
## 10:      0.59375        0.01100 58     0.78 :(
## 11:      0.65625       -0.05500 58     0.065 .
## 12:      0.71875       -0.10000 58 0.00011 ***
## 13:      0.78125       -0.15000 57 7.8e-08 ***
## 14:      0.84375       -0.18000 55 4.5e-08 ***
## 15:      0.90625       -0.20000 57 4.9e-11 ***
## 16:      0.96875       -0.17000 57 4.9e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 49     0.84 :(
##  2: 57     0.21 :(
##  3: 57     0.33 :(
##  4: 57     0.46 :(
##  5: 58     0.56 :(
##  6: 57     0.94 :(
##  7: 56     0.82 :(
##  8: 58     0.65 :(
##  9: 56     0.84 :(
## 10: 58     0.78 :(
## 11: 58     0.065 .
## 12: 58 0.00011 ***
## 13: 57 7.8e-08 ***
## 14: 55 4.5e-08 ***
## 15: 57 4.9e-11 ***
## 16: 57 4.9e-11 ***
## [1] 56.6
## [1] 2.19

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0044 30     0.93 :(
##  2:      0.09375         0.0045 32     0.61 :(
##  3:      0.15625        -0.0130 40     0.39 :(
##  4:      0.21875        -0.0044 40     0.88 :(
##  5:      0.28125        -0.0074 36     0.88 :(
##  6:      0.34375         0.0370 36     0.43 :(
##  7:      0.40625         0.0400 39     0.36 :(
##  8:      0.46875         0.0670 36     0.32 :(
##  9:      0.53125         0.0470 36     0.24 :(
## 10:      0.59375         0.0130 39     0.52 :(
## 11:      0.65625        -0.0130 34     0.69 :(
## 12:      0.71875        -0.1300 35    0.002 **
## 13:      0.78125        -0.1500 35   0.0018 **
## 14:      0.84375        -0.1800 22   3e-04 ***
## 15:      0.90625        -0.1900 22 3.9e-05 ***
## 16:      0.96875        -0.1500 11   0.0037 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 30     0.93 :(
##  2: 32     0.61 :(
##  3: 40     0.39 :(
##  4: 40     0.88 :(
##  5: 36     0.88 :(
##  6: 36     0.43 :(
##  7: 39     0.36 :(
##  8: 36     0.32 :(
##  9: 36     0.24 :(
## 10: 39     0.52 :(
## 11: 34     0.69 :(
## 12: 35    0.002 **
## 13: 35   0.0018 **
## 14: 22   3e-04 ***
## 15: 22 3.9e-05 ***
## 16: 11   0.0037 **
## [1] 32.7
## [1] 7.96

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 29     0.18 :(
##  2:      0.09375         0.0130 33     0.36 :(
##  3:      0.15625         0.0220 31     0.61 :(
##  4:      0.21875        -0.0045 35     0.78 :(
##  5:      0.28125        -0.0670 34     0.12 :(
##  6:      0.34375        -0.0820 37     0.11 :(
##  7:      0.40625        -0.0620 36      0.05 .
##  8:      0.46875        -0.1300 37     0.018 *
##  9:      0.53125         0.0220 34     0.68 :(
## 10:      0.59375        -0.0018 37        1 :(
## 11:      0.65625        -0.0970 36     0.026 *
## 12:      0.71875        -0.0900 37     0.041 *
## 13:      0.78125        -0.1300 38 0.00075 ***
## 14:      0.84375        -0.1800 36 5.6e-05 ***
## 15:      0.90625        -0.2300 37 1.1e-07 ***
## 16:      0.96875        -0.2100 34 3.6e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 29     0.18 :(
##  2: 33     0.36 :(
##  3: 31     0.61 :(
##  4: 35     0.78 :(
##  5: 34     0.12 :(
##  6: 37     0.11 :(
##  7: 36      0.05 .
##  8: 37     0.018 *
##  9: 34     0.68 :(
## 10: 37        1 :(
## 11: 36     0.026 *
## 12: 37     0.041 *
## 13: 38 0.00075 ***
## 14: 36 5.6e-05 ***
## 15: 37 1.1e-07 ***
## 16: 34 3.6e-07 ***
## [1] 35.1
## [1] 2.46

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310  9     0.53 :(
##  2:      0.09375        -0.0220 19     0.76 :(
##  3:      0.15625        -0.0700 18     0.21 :(
##  4:      0.21875        -0.0045 17     0.78 :(
##  5:      0.28125         0.0400 18     0.69 :(
##  6:      0.34375         0.0850 16     0.42 :(
##  7:      0.40625         0.1200 20     0.049 *
##  8:      0.46875         0.0760 19     0.61 :(
##  9:      0.53125        -0.1000 18      0.3 :(
## 10:      0.59375        -0.1100 18     0.19 :(
## 11:      0.65625        -0.0850 22     0.27 :(
## 12:      0.71875        -0.0760 21     0.055 .
## 13:      0.78125        -0.1000 20     0.019 *
## 14:      0.84375        -0.1400 26   0.0053 **
## 15:      0.90625        -0.1500 26 7.6e-06 ***
## 16:      0.96875        -0.1600 26 8.5e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.53 :(
##  2: 19     0.76 :(
##  3: 18     0.21 :(
##  4: 17     0.78 :(
##  5: 18     0.69 :(
##  6: 16     0.42 :(
##  7: 20     0.049 *
##  8: 19     0.61 :(
##  9: 18      0.3 :(
## 10: 18     0.19 :(
## 11: 22     0.27 :(
## 12: 21     0.055 .
## 13: 20     0.019 *
## 14: 26   0.0053 **
## 15: 26 7.6e-06 ***
## 16: 26 8.5e-06 ***
## [1] 19.6
## [1] 4.27

Motor task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375         -0.094  8     0.21 :(
##  3:      0.15625         -0.099 26     0.015 *
##  4:      0.21875         -0.076 40   0.0065 **
##  5:      0.28125         -0.067 45     0.055 .
##  6:      0.34375         -0.058 47     0.21 :(
##  7:      0.40625         -0.013 49      0.8 :(
##  8:      0.46875          0.031 49     0.73 :(
##  9:      0.53125          0.076 51     0.15 :(
## 10:      0.59375          0.025 51     0.55 :(
## 11:      0.65625         -0.013 53     0.45 :(
## 12:      0.71875         -0.052 51     0.079 .
## 13:      0.78125         -0.067 44     0.029 *
## 14:      0.84375         -0.094 27   0.0073 **
## 15:      0.90625         -0.078 14 0.00076 ***
## 16:      0.96875         -0.110  6     0.034 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.21 :(
##  2: 26     0.015 *
##  3: 40   0.0065 **
##  4: 45     0.055 .
##  5: 47     0.21 :(
##  6: 49      0.8 :(
##  7: 49     0.73 :(
##  8: 51     0.15 :(
##  9: 51     0.55 :(
## 10: 53     0.45 :(
## 11: 51     0.079 .
## 12: 44     0.029 *
## 13: 27   0.0073 **
## 14: 14 0.00076 ***
## 15:  6     0.034 *
## [1] 37.4
## [1] 16.7

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n     pval
##  1:      0.03125             NA  0       NA
##  2:      0.09375        -0.0940  8  0.21 :(
##  3:      0.15625        -0.1200 24 0.005 **
##  4:      0.21875        -0.0760 26  0.031 *
##  5:      0.28125        -0.0670 25  0.12 :(
##  6:      0.34375         0.0130 26   0.8 :(
##  7:      0.40625         0.0320 25  0.67 :(
##  8:      0.46875         0.0880 24  0.14 :(
##  9:      0.53125         0.0760 23  0.21 :(
## 10:      0.59375         0.0970 24  0.038 *
## 11:      0.65625         0.0081 25  0.94 :(
## 12:      0.71875        -0.0470 22  0.078 .
## 13:      0.78125        -0.1000 15  0.26 :(
## 14:      0.84375             NA  0       NA
## 15:      0.90625             NA  0       NA
## 16:      0.96875             NA  0       NA
## [1] "mean and sd of nb players per bin"
##     nb     pval
##  1:  8  0.21 :(
##  2: 24 0.005 **
##  3: 26  0.031 *
##  4: 25  0.12 :(
##  5: 26   0.8 :(
##  6: 25  0.67 :(
##  7: 24  0.14 :(
##  8: 23  0.21 :(
##  9: 24  0.038 *
## 10: 25  0.94 :(
## 11: 22  0.078 .
## 12: 15  0.26 :(
## [1] 22.2
## [1] 5.36

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625         0.2000  2      1 :(
##  4:      0.21875        -0.2200 14   0.15 :(
##  5:      0.28125        -0.0990 20   0.38 :(
##  6:      0.34375        -0.1600 20    0.08 .
##  7:      0.40625        -0.0490 22   0.31 :(
##  8:      0.46875        -0.0160 21   0.63 :(
##  9:      0.53125         0.1400 21 0.0048 **
## 10:      0.59375         0.0130 21   0.86 :(
## 11:      0.65625        -0.0130 21   0.94 :(
## 12:      0.71875         0.0430 22   0.43 :(
## 13:      0.78125        -0.0099 21   0.75 :(
## 14:      0.84375        -0.0940 19   0.017 *
## 15:      0.90625             NA  6        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.15 :(
##  3: 20   0.38 :(
##  4: 20    0.08 .
##  5: 22   0.31 :(
##  6: 21   0.63 :(
##  7: 21 0.0048 **
##  8: 21   0.86 :(
##  9: 21   0.94 :(
## 10: 22   0.43 :(
## 11: 21   0.75 :(
## 12: 19   0.017 *
## [1] 18.7
## [1] 5.66

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625         -0.049 2    1 :(
##  8:      0.46875         -0.180 4 0.58 :(
##  9:      0.53125         -0.400 7 0.071 .
## 10:      0.59375         -0.290 6 0.14 :(
## 11:      0.65625         -0.230 7 0.16 :(
## 12:      0.71875         -0.250 7 0.047 *
## 13:      0.78125         -0.180 8 0.023 *
## 14:      0.84375         -0.110 8 0.29 :(
## 15:      0.90625         -0.110 8 0.013 *
## 16:      0.96875         -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  2    1 :(
##  2:  4 0.58 :(
##  3:  7 0.071 .
##  4:  6 0.14 :(
##  5:  7 0.16 :(
##  6:  7 0.047 *
##  7:  8 0.023 *
##  8:  8 0.29 :(
##  9:  8 0.013 *
## 10:  6 0.034 *
## [1] 6.3
## [1] 1.95

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 44     0.033 *
##  2:      0.09375         -0.094 53     0.014 *
##  3:      0.15625         -0.071 48     0.046 *
##  4:      0.21875         -0.040 40     0.21 :(
##  5:      0.28125         -0.067 38     0.42 :(
##  6:      0.34375         -0.058 36     0.21 :(
##  7:      0.40625         -0.049 37     0.53 :(
##  8:      0.46875         -0.110 37     0.033 *
##  9:      0.53125         -0.140 30     0.027 *
## 10:      0.59375         -0.170 33     0.029 *
## 11:      0.65625         -0.085 34     0.029 *
## 12:      0.71875         -0.150 34   0.0034 **
## 13:      0.78125         -0.210 38 0.00063 ***
## 14:      0.84375         -0.150 45 8.4e-05 ***
## 15:      0.90625         -0.170 53 1.7e-10 ***
## 16:      0.96875         -0.140 56 6.3e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44     0.033 *
##  2: 53     0.014 *
##  3: 48     0.046 *
##  4: 40     0.21 :(
##  5: 38     0.42 :(
##  6: 36     0.21 :(
##  7: 37     0.53 :(
##  8: 37     0.033 *
##  9: 30     0.027 *
## 10: 33     0.029 *
## 11: 34     0.029 *
## 12: 34   0.0034 **
## 13: 38 0.00063 ***
## 14: 45 8.4e-05 ***
## 15: 53 1.7e-10 ***
## 16: 56 6.3e-11 ***
## [1] 41
## [1] 7.94

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125         -0.031 11   0.82 :(
##  2:      0.09375         -0.094 10   0.084 .
##  3:      0.15625         -0.085 11   0.38 :(
##  4:      0.21875         -0.076  7   0.27 :(
##  5:      0.28125          0.063 11   0.62 :(
##  6:      0.34375         -0.240  7   0.021 *
##  7:      0.40625         -0.190  7    0.2 :(
##  8:      0.46875         -0.170  7   0.15 :(
##  9:      0.53125         -0.100  6   0.53 :(
## 10:      0.59375         -0.210  7   0.11 :(
## 11:      0.65625         -0.230  6   0.29 :(
## 12:      0.71875         -0.430  6   0.036 *
## 13:      0.78125         -0.320  7   0.078 .
## 14:      0.84375         -0.130  8   0.11 :(
## 15:      0.90625         -0.120 10 0.0053 **
## 16:      0.96875         -0.150 11 0.0035 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 11   0.82 :(
##  2: 10   0.084 .
##  3: 11   0.38 :(
##  4:  7   0.27 :(
##  5: 11   0.62 :(
##  6:  7   0.021 *
##  7:  7    0.2 :(
##  8:  7   0.15 :(
##  9:  6   0.53 :(
## 10:  7   0.11 :(
## 11:  6   0.29 :(
## 12:  6   0.036 *
## 13:  7   0.078 .
## 14:  8   0.11 :(
## 15: 10 0.0053 **
## 16: 11 0.0035 **
## [1] 8.25
## [1] 2.02

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 24     0.012 *
##  2:      0.09375         -0.094 25     0.011 *
##  3:      0.15625         -0.085 21     0.076 .
##  4:      0.21875         -0.076 18     0.16 :(
##  5:      0.28125         -0.140 14     0.52 :(
##  6:      0.34375         -0.022 17     0.96 :(
##  7:      0.40625         -0.120 17     0.046 *
##  8:      0.46875         -0.180 19     0.015 *
##  9:      0.53125         -0.250 15     0.031 *
## 10:      0.59375         -0.170 17     0.071 .
## 11:      0.65625         -0.190 15     0.028 *
## 12:      0.71875         -0.220 13     0.017 *
## 13:      0.78125         -0.210 20   0.0023 **
## 14:      0.84375         -0.240 19   0.0013 **
## 15:      0.90625         -0.220 25 1.2e-05 ***
## 16:      0.96875         -0.150 25 1.2e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 24     0.012 *
##  2: 25     0.011 *
##  3: 21     0.076 .
##  4: 18     0.16 :(
##  5: 14     0.52 :(
##  6: 17     0.96 :(
##  7: 17     0.046 *
##  8: 19     0.015 *
##  9: 15     0.031 *
## 10: 17     0.071 .
## 11: 15     0.028 *
## 12: 13     0.017 *
## 13: 20   0.0023 **
## 14: 19   0.0013 **
## 15: 25 1.2e-05 ***
## 16: 25 1.2e-05 ***
## [1] 19
## [1] 4.03

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310  9     0.53 :(
##  2:      0.09375        -0.0220 18     0.89 :(
##  3:      0.15625        -0.0130 16     0.38 :(
##  4:      0.21875         0.0190 15      0.8 :(
##  5:      0.28125        -0.1000 13     0.48 :(
##  6:      0.34375        -0.0220 12     0.91 :(
##  7:      0.40625         0.1700 13     0.091 .
##  8:      0.46875         0.1000 11      0.5 :(
##  9:      0.53125        -0.0310  9     0.63 :(
## 10:      0.59375        -0.0220  9     0.91 :(
## 11:      0.65625        -0.0130 13     0.78 :(
## 12:      0.71875        -0.0045 15     0.67 :(
## 13:      0.78125        -0.0063 11        1 :(
## 14:      0.84375        -0.0580 18     0.18 :(
## 15:      0.90625        -0.1400 18 0.00016 ***
## 16:      0.96875        -0.1200 20 9.2e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.53 :(
##  2: 18     0.89 :(
##  3: 16     0.38 :(
##  4: 15      0.8 :(
##  5: 13     0.48 :(
##  6: 12     0.91 :(
##  7: 13     0.091 .
##  8: 11      0.5 :(
##  9:  9     0.63 :(
## 10:  9     0.91 :(
## 11: 13     0.78 :(
## 12: 15     0.67 :(
## 13: 11        1 :(
## 14: 18     0.18 :(
## 15: 18 0.00016 ***
## 16: 20 9.2e-05 ***
## [1] 13.8
## [1] 3.55

Logical task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0045 35     0.79 :(
##  2:      0.09375         0.1100 40     0.012 *
##  3:      0.15625         0.1100 40     0.097 .
##  4:      0.21875         0.1600 42   0.0092 **
##  5:      0.28125         0.1500 34     0.051 .
##  6:      0.34375         0.0850 39     0.21 :(
##  7:      0.40625         0.0220 44     0.18 :(
##  8:      0.46875        -0.0045 39     0.93 :(
##  9:      0.53125        -0.0310 37     0.71 :(
## 10:      0.59375        -0.0220 41     0.61 :(
## 11:      0.65625        -0.0490 39     0.42 :(
## 12:      0.71875        -0.1500 38   0.0068 **
## 13:      0.78125        -0.1700 43 0.00035 ***
## 14:      0.84375        -0.2400 41 1.8e-07 ***
## 15:      0.90625        -0.2800 40 3.6e-08 ***
## 16:      0.96875        -0.3300 25 1.3e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.79 :(
##  2: 40     0.012 *
##  3: 40     0.097 .
##  4: 42   0.0092 **
##  5: 34     0.051 .
##  6: 39     0.21 :(
##  7: 44     0.18 :(
##  8: 39     0.93 :(
##  9: 37     0.71 :(
## 10: 41     0.61 :(
## 11: 39     0.42 :(
## 12: 38   0.0068 **
## 13: 43 0.00035 ***
## 14: 41 1.8e-07 ***
## 15: 40 3.6e-08 ***
## 16: 25 1.3e-05 ***
## [1] 38.6
## [1] 4.47

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0044 26      0.9 :(
##  2:      0.09375         0.0370 26     0.24 :(
##  3:      0.15625         0.0940 24     0.14 :(
##  4:      0.21875         0.1600 24     0.036 *
##  5:      0.28125         0.1100 17     0.32 :(
##  6:      0.34375         0.0850 21     0.24 :(
##  7:      0.40625         0.0940 22     0.25 :(
##  8:      0.46875         0.0670 20     0.44 :(
##  9:      0.53125         0.0400 18     0.46 :(
## 10:      0.59375        -0.0220 21     0.42 :(
## 11:      0.65625        -0.0130 17     0.57 :(
## 12:      0.71875        -0.1500 18     0.097 .
## 13:      0.78125        -0.1400 21     0.026 *
## 14:      0.84375        -0.2000 18 0.00057 ***
## 15:      0.90625        -0.2600 15 0.00071 ***
## 16:      0.96875             NA  1          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 26      0.9 :(
##  2: 26     0.24 :(
##  3: 24     0.14 :(
##  4: 24     0.036 *
##  5: 17     0.32 :(
##  6: 21     0.24 :(
##  7: 22     0.25 :(
##  8: 20     0.44 :(
##  9: 18     0.46 :(
## 10: 21     0.42 :(
## 11: 17     0.57 :(
## 12: 18     0.097 .
## 13: 21     0.026 *
## 14: 18 0.00057 ***
## 15: 15 0.00071 ***
## [1] 20.5
## [1] 3.4

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.064  9      0.9 :(
##  2:      0.09375          0.310 13     0.014 *
##  3:      0.15625          0.240 13     0.16 :(
##  4:      0.21875          0.210 15     0.056 .
##  5:      0.28125          0.076 11     0.31 :(
##  6:      0.34375         -0.033 12        1 :(
##  7:      0.40625         -0.049 15     0.75 :(
##  8:      0.46875         -0.064 12     0.36 :(
##  9:      0.53125         -0.070 11     0.45 :(
## 10:      0.59375          0.085 12     0.22 :(
## 11:      0.65625         -0.160 14     0.19 :(
## 12:      0.71875         -0.290 14     0.032 *
## 13:      0.78125         -0.160 15     0.021 *
## 14:      0.84375         -0.260 15   0.0013 **
## 15:      0.90625         -0.320 15 0.00072 ***
## 16:      0.96875         -0.340 14   0.0011 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9      0.9 :(
##  2: 13     0.014 *
##  3: 13     0.16 :(
##  4: 15     0.056 .
##  5: 11     0.31 :(
##  6: 12        1 :(
##  7: 15     0.75 :(
##  8: 12     0.36 :(
##  9: 11     0.45 :(
## 10: 12     0.22 :(
## 11: 14     0.19 :(
## 12: 14     0.032 *
## 13: 15     0.021 *
## 14: 15   0.0013 **
## 15: 15 0.00072 ***
## 16: 14   0.0011 **
## [1] 13.1
## [1] 1.82

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  1        NA
##  3:      0.15625             NA  3        NA
##  4:      0.21875        -0.0280  3      1 :(
##  5:      0.28125         0.1500  6   0.13 :(
##  6:      0.34375         0.1600  6    0.4 :(
##  7:      0.40625         0.0460  7   0.15 :(
##  8:      0.46875        -0.0016  7      1 :(
##  9:      0.53125        -0.1000  8   0.44 :(
## 10:      0.59375        -0.1700  8   0.36 :(
## 11:      0.65625         0.1300  8   0.29 :(
## 12:      0.71875        -0.1000  6   0.67 :(
## 13:      0.78125        -0.2100  7    0.2 :(
## 14:      0.84375        -0.2700  8   0.042 *
## 15:      0.90625        -0.2600 10 0.0059 **
## 16:      0.96875        -0.3100 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  3      1 :(
##  2:  6   0.13 :(
##  3:  6    0.4 :(
##  4:  7   0.15 :(
##  5:  7      1 :(
##  6:  8   0.44 :(
##  7:  8   0.36 :(
##  8:  8   0.29 :(
##  9:  6   0.67 :(
## 10:  7    0.2 :(
## 11:  8   0.042 *
## 12: 10 0.0059 **
## 13: 10 0.0059 **
## [1] 7.23
## [1] 1.83

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85521  -0.20000   0.03999   0.20805   0.69174  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.02757    0.02336  -1.180   0.2381   
## timeNorm     0.03482    0.02460   1.416   0.1570   
## obj.diff    -0.08659    0.03066  -2.824   0.0048 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07429011)
## 
##     Null deviance: 121.29  on 1623  degrees of freedom
## Residual deviance: 120.42  on 1621  degrees of freedom
## AIC: 391.67
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78610  -0.11567   0.04559   0.11403   0.81494  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.001581   0.016841   0.094    0.925    
## timeNorm     0.009074   0.022514   0.403    0.687    
## obj.diff    -0.212404   0.017421 -12.192   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06402259)
## 
##     Null deviance: 113.31  on 1623  degrees of freedom
## Residual deviance: 103.78  on 1621  degrees of freedom
## AIC: 150.11
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70209  -0.22699   0.01746   0.22707   0.66363  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.179866   0.023643   7.608 4.89e-14 ***
## timeNorm     0.007075   0.029400   0.241     0.81    
## obj.diff    -0.476741   0.025105 -18.990  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09502739)
## 
##     Null deviance: 179.81  on 1507  degrees of freedom
## Residual deviance: 143.02  on 1505  degrees of freedom
## AIC: 735.3
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.5089286     0.6008109 -0.07683886 112 0.0057 **
##  2:      4.5      0.4889456     0.5714407 -0.07445527 168 8e-04 ***
##  3:      7.5      0.4863946     0.5416953 -0.04763643 168   0.023 *
##  4:     10.5      0.5008503     0.5401276 -0.03488016 168   0.13 :(
##  5:     13.5      0.4447279     0.5174551 -0.06672273 168 0.0017 **
##  6:     16.5      0.4931973     0.5305272 -0.02102698 168   0.36 :(
##  7:     19.5      0.4736395     0.5315528 -0.04770887 168   0.021 *
##  8:     22.5      0.4455782     0.4897264 -0.03529116 168   0.093 .
##  9:     25.5      0.4464286     0.4805683 -0.02474658 168   0.31 :(
## 10:     28.5      0.4166667     0.4572889 -0.03958163 168   0.083 .
##     time  error.diff shapes
##  1:  1.5 -0.07683886     24
##  2:  4.5 -0.07445527     24
##  3:  7.5 -0.04763643     24
##  4: 10.5 -0.03488016     16
##  5: 13.5 -0.06672273     24
##  6: 16.5 -0.02102698     16
##  7: 19.5 -0.04770887     24
##  8: 22.5 -0.03529116     16
##  9: 25.5 -0.02474658     16
## 10: 28.5 -0.03958163     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4285714     0.5941293 -0.14841500 112 7.1e-09 ***
##  2:      4.5      0.5212585     0.6104788 -0.09151470 168 4.2e-08 ***
##  3:      7.5      0.4379252     0.5299114 -0.09240609 168 1.5e-07 ***
##  4:     10.5      0.4642857     0.5824635 -0.10424800 168 1.8e-11 ***
##  5:     13.5      0.4302721     0.5656294 -0.11814246 168 2.1e-13 ***
##  6:     16.5      0.4064626     0.5333505 -0.11438030 168 4.2e-11 ***
##  7:     19.5      0.4685374     0.5641391 -0.08414982 168 1.9e-08 ***
##  8:     22.5      0.4311224     0.5656705 -0.12484954 168 2.4e-12 ***
##  9:     25.5      0.4923469     0.5874740 -0.09752555 168 7.2e-11 ***
## 10:     28.5      0.4608844     0.5711020 -0.10647805 168 1.2e-10 ***
##     time  error.diff shapes
##  1:  1.5 -0.14841500     24
##  2:  4.5 -0.09151470     24
##  3:  7.5 -0.09240609     24
##  4: 10.5 -0.10424800     24
##  5: 13.5 -0.11814246     24
##  6: 16.5 -0.11438030     24
##  7: 19.5 -0.08414982     24
##  8: 22.5 -0.12484954     24
##  9: 25.5 -0.09752555     24
## 10: 28.5 -0.10647805     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4354396     0.5969130 -0.146652665 104 1.6e-06 ***
##  2:      4.5      0.5027473     0.6297636 -0.123587134 156 4.8e-06 ***
##  3:      7.5      0.4972527     0.5544687 -0.063129356 156     0.013 *
##  4:     10.5      0.4908425     0.5229882 -0.045103811 156     0.074 .
##  5:     13.5      0.4734432     0.5312208 -0.047826904 156     0.091 .
##  6:     16.5      0.4661172     0.5008164 -0.043716799 156     0.089 .
##  7:     19.5      0.3937729     0.4456698 -0.053440226 156     0.047 *
##  8:     22.5      0.3864469     0.4198655 -0.032783828 156     0.22 :(
##  9:     25.5      0.3800366     0.3963862 -0.012966789 156     0.66 :(
## 10:     28.5      0.3864469     0.3637653 -0.007979433 156     0.82 :(
##     time   error.diff shapes
##  1:  1.5 -0.146652665     24
##  2:  4.5 -0.123587134     24
##  3:  7.5 -0.063129356     24
##  4: 10.5 -0.045103811     16
##  5: 13.5 -0.047826904     16
##  6: 16.5 -0.043716799     16
##  7: 19.5 -0.053440226     24
##  8: 22.5 -0.032783828     16
##  9: 25.5 -0.012966789     16
## 10: 28.5 -0.007979433     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.76605  -0.17974   0.09827   0.16562   0.72448  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07622    0.03024   2.521   0.0119 *  
## timeNorm     0.03377    0.03261   1.036   0.3005    
## obj.diff    -0.32572    0.03080 -10.575   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09040828)
## 
##     Null deviance: 109.823  on 1101  degrees of freedom
## Residual deviance:  99.359  on 1099  degrees of freedom
## AIC: 483.77
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77371  -0.18878   0.04154   0.20265   0.74395  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05416    0.01987   2.726  0.00648 ** 
## timeNorm     0.02489    0.02395   1.039  0.29882    
## obj.diff    -0.28152    0.02237 -12.584  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08063932)
## 
##     Null deviance: 160.31  on 1826  degrees of freedom
## Residual deviance: 147.09  on 1824  degrees of freedom
## AIC: 589.84
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74927  -0.18962  -0.03788   0.19768   0.76912  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05189    0.01808   2.870  0.00415 ** 
## timeNorm     0.03130    0.02302   1.359  0.17423    
## obj.diff    -0.22828    0.02353  -9.702  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07220067)
## 
##     Null deviance: 139.33  on 1826  degrees of freedom
## Residual deviance: 131.69  on 1824  degrees of freedom
## AIC: 387.88
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.5281955     0.7253320 -0.18770872  76 8.8e-08 ***
##  2:      4.5      0.6190476     0.7689197 -0.12239921 114 4.8e-07 ***
##  3:      7.5      0.5877193     0.7094956 -0.11127265 114 1.1e-05 ***
##  4:     10.5      0.5614035     0.6993212 -0.11956311 114 4.2e-06 ***
##  5:     13.5      0.5789474     0.7269567 -0.12152900 114 8.4e-08 ***
##  6:     16.5      0.5375940     0.6678003 -0.11156080 114 4.8e-06 ***
##  7:     19.5      0.5664160     0.6813782 -0.08953959 114   0.0011 **
##  8:     22.5      0.5626566     0.7025643 -0.11945525 114 1.6e-06 ***
##  9:     25.5      0.5162907     0.6530197 -0.11174022 114 6.7e-07 ***
## 10:     28.5      0.5852130     0.6646574 -0.07927214 114 0.00029 ***
##     time  error.diff shapes
##  1:  1.5 -0.18770872     24
##  2:  4.5 -0.12239921     24
##  3:  7.5 -0.11127265     24
##  4: 10.5 -0.11956311     24
##  5: 13.5 -0.12152900     24
##  6: 16.5 -0.11156080     24
##  7: 19.5 -0.08953959     24
##  8: 22.5 -0.11945525     24
##  9: 25.5 -0.11174022     24
## 10: 28.5 -0.07927214     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4659864     0.6122964 -0.12648243 126 3.2e-06 ***
##  2:      4.5      0.4958428     0.6315007 -0.12319809 189 1.4e-10 ***
##  3:      7.5      0.4580499     0.5313254 -0.07335336 189 6.1e-05 ***
##  4:     10.5      0.5245654     0.5829560 -0.06767141 189 0.00041 ***
##  5:     13.5      0.4580499     0.5530913 -0.08538869 189 5.9e-05 ***
##  6:     16.5      0.4777022     0.5603358 -0.08178293 189 0.00022 ***
##  7:     19.5      0.4671202     0.5589971 -0.08014481 189 3.5e-05 ***
##  8:     22.5      0.4058957     0.5023611 -0.09726237 189 8.4e-06 ***
##  9:     25.5      0.4761905     0.5383267 -0.06695149 189   0.0048 **
## 10:     28.5      0.4331066     0.4988396 -0.07033125 189 0.00069 ***
##     time  error.diff shapes
##  1:  1.5 -0.12648243     24
##  2:  4.5 -0.12319809     24
##  3:  7.5 -0.07335336     24
##  4: 10.5 -0.06767141     24
##  5: 13.5 -0.08538869     24
##  6: 16.5 -0.08178293     24
##  7: 19.5 -0.08014481     24
##  8: 22.5 -0.09726237     24
##  9: 25.5 -0.06695149     24
## 10: 28.5 -0.07033125     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.4081633     0.5050608 -0.08630678 126 5e-04 ***
##  2:      4.5      0.4436886     0.4751064 -0.03789574 189   0.11 :(
##  3:      7.5      0.4195011     0.4509208 -0.03026032 189   0.15 :(
##  4:     10.5      0.3998488     0.4247627 -0.03200187 189   0.14 :(
##  5:     13.5      0.3613001     0.4096366 -0.05160912 189    0.01 *
##  6:     16.5      0.3824641     0.3959053 -0.01776122 189   0.36 :(
##  7:     19.5      0.3537415     0.3718157 -0.02981185 189   0.12 :(
##  8:     22.5      0.3529856     0.3585562 -0.01201852 189   0.56 :(
##  9:     25.5      0.3605442     0.3443352  0.00846663 189   0.75 :(
## 10:     28.5      0.3129252     0.3146320 -0.02201649 189   0.27 :(
##     time  error.diff shapes
##  1:  1.5 -0.08630678     24
##  2:  4.5 -0.03789574     16
##  3:  7.5 -0.03026032     16
##  4: 10.5 -0.03200187     16
##  5: 13.5 -0.05160912     24
##  6: 16.5 -0.01776122     16
##  7: 19.5 -0.02981185     16
##  8: 22.5 -0.01201852     16
##  9: 25.5  0.00846663     16
## 10: 28.5 -0.02201649     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77782  -0.16141   0.07773   0.18154   0.65784  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.462332   0.119085  -3.882 0.000135 ***
## timeNorm     0.004994   0.071725   0.070 0.944555    
## obj.diff     0.309216   0.135862   2.276 0.023773 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09109535)
## 
##     Null deviance: 21.338  on 231  degrees of freedom
## Residual deviance: 20.861  on 229  degrees of freedom
## AIC: 107.53
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean error.diff  n        pval
##  1:      1.5      0.6339286     0.8544830 -0.1813070 16   0.0013 **
##  2:      4.5      0.5773810     0.7995145 -0.1900501 24 0.00057 ***
##  3:      7.5      0.5714286     0.7551085 -0.1583598 24   0.0043 **
##  4:     10.5      0.5892857     0.7836615 -0.1770491 24   0.0011 **
##  5:     13.5      0.6071429     0.8240112 -0.1620422 24   0.0018 **
##  6:     16.5      0.4821429     0.7818411 -0.2673553 24 0.00028 ***
##  7:     19.5      0.5000000     0.7263256 -0.2097781 24   0.0096 **
##  8:     22.5      0.6130952     0.7654436 -0.1099361 24     0.11 :(
##  9:     25.5      0.5119048     0.7908307 -0.2703569 24 0.00018 ***
## 10:     28.5      0.5476190     0.7394768 -0.1501698 24   0.0087 **
##     time error.diff shapes
##  1:  1.5 -0.1813070     24
##  2:  4.5 -0.1900501     24
##  3:  7.5 -0.1583598     24
##  4: 10.5 -0.1770491     24
##  5: 13.5 -0.1620422     24
##  6: 16.5 -0.2673553     24
##  7: 19.5 -0.2097781     24
##  8: 22.5 -0.1099361     16
##  9: 25.5 -0.2703569     24
## 10: 28.5 -0.1501698     24

## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81780  -0.19138   0.04321   0.17708   0.68822  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.13079    0.04059  -3.222  0.00134 **
## timeNorm     0.07430    0.03785   1.963  0.05008 . 
## obj.diff     0.09494    0.05455   1.740  0.08228 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06868557)
## 
##     Null deviance: 44.015  on 637  degrees of freedom
## Residual deviance: 43.615  on 635  degrees of freedom
## AIC: 106.86
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.5227273     0.6251419 -0.087913622 44 0.062 .
##  2:      4.5      0.5432900     0.6224524 -0.067787288 66 0.053 .
##  3:      7.5      0.5064935     0.5482212 -0.033170955 66 0.34 :(
##  4:     10.5      0.5519481     0.5744464 -0.017320378 66  0.7 :(
##  5:     13.5      0.5086580     0.5455378 -0.027725556 66 0.47 :(
##  6:     16.5      0.5519481     0.5560045  0.008925402 66 0.85 :(
##  7:     19.5      0.5519481     0.5704673 -0.010678355 66 0.76 :(
##  8:     22.5      0.4307359     0.5060978 -0.079405018 66 0.035 *
##  9:     25.5      0.4870130     0.4999714 -0.012031106 66 0.76 :(
## 10:     28.5      0.4870130     0.5016324 -0.017813543 66 0.61 :(
##     time   error.diff shapes
##  1:  1.5 -0.087913622     16
##  2:  4.5 -0.067787288     16
##  3:  7.5 -0.033170955     16
##  4: 10.5 -0.017320378     16
##  5: 13.5 -0.027725556     16
##  6: 16.5  0.008925402     16
##  7: 19.5 -0.010678355     16
##  8: 22.5 -0.079405018     24
##  9: 25.5 -0.012031106     16
## 10: 28.5 -0.017813543     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7823  -0.1833   0.0022   0.2021   0.7030  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.08025    0.03159  -2.541   0.0113 *
## timeNorm     0.06361    0.03395   1.874   0.0613 .
## obj.diff     0.06975    0.04864   1.434   0.1520  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06414613)
## 
##     Null deviance: 48.468  on 753  degrees of freedom
## Residual deviance: 48.174  on 751  degrees of freedom
## AIC: 73.823
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.4587912     0.5021701 -0.029565591 52  0.5 :(
##  2:      4.5      0.4157509     0.4581003 -0.036201957 78 0.23 :(
##  3:      7.5      0.4432234     0.4705078 -0.025469870 78 0.34 :(
##  4:     10.5      0.4304029     0.4361551 -0.003198821 78 0.91 :(
##  5:     13.5      0.3406593     0.3993679 -0.061812903 78 0.028 *
##  6:     16.5      0.4468864     0.4316421  0.017600071 78 0.45 :(
##  7:     19.5      0.3992674     0.4386951 -0.038308162 78  0.2 :(
##  8:     22.5      0.4065934     0.3910376  0.012148843 78 0.56 :(
##  9:     25.5      0.3919414     0.3686849  0.027863007 78 0.37 :(
## 10:     28.5      0.3168498     0.3329405 -0.021946631 78 0.56 :(
##     time   error.diff shapes
##  1:  1.5 -0.029565591     16
##  2:  4.5 -0.036201957     16
##  3:  7.5 -0.025469870     16
##  4: 10.5 -0.003198821     16
##  5: 13.5 -0.061812903     24
##  6: 16.5  0.017600071     16
##  7: 19.5 -0.038308162     16
##  8: 22.5  0.012148843     16
##  9: 25.5  0.027863007     16
## 10: 28.5 -0.021946631     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.82141  -0.15374   0.03313   0.11265   0.74133  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06993    0.03099   2.256   0.0244 *  
## timeNorm     0.01707    0.03894   0.438   0.6612    
## obj.diff    -0.24589    0.03153  -7.798 2.95e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06836725)
## 
##     Null deviance: 43.628  on 579  degrees of freedom
## Residual deviance: 39.448  on 577  degrees of freedom
## AIC: 94.901
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4785714     0.5999008 -0.13404889 40   0.0058 **
##  2:      4.5      0.6547619     0.7009436 -0.07926068 60     0.048 *
##  3:      7.5      0.5523810     0.6171368 -0.08578639 60     0.059 .
##  4:     10.5      0.5261905     0.6349960 -0.10209314 60   0.0039 **
##  5:     13.5      0.5452381     0.6718447 -0.11959740 60 4.8e-06 ***
##  6:     16.5      0.5214286     0.5575435 -0.07312179 60     0.094 .
##  7:     19.5      0.5952381     0.6401329 -0.06573366 60     0.11 :(
##  8:     22.5      0.5833333     0.6695307 -0.11887633 60   0.0015 **
##  9:     25.5      0.4928571     0.5891457 -0.10298824 60 3.6e-06 ***
## 10:     28.5      0.5809524     0.6285546 -0.08367159 60     0.015 *
##     time  error.diff shapes
##  1:  1.5 -0.13404889     24
##  2:  4.5 -0.07926068     24
##  3:  7.5 -0.08578639     16
##  4: 10.5 -0.10209314     24
##  5: 13.5 -0.11959740     24
##  6: 16.5 -0.07312179     16
##  7: 19.5 -0.06573366     16
##  8: 22.5 -0.11887633     24
##  9: 25.5 -0.10298824     24
## 10: 28.5 -0.08367159     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7480  -0.1019   0.0036   0.1302   0.8064  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03649    0.02404  -1.518    0.130    
## timeNorm     0.02091    0.03262   0.641    0.522    
## obj.diff    -0.21518    0.02506  -8.586   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05998238)
## 
##     Null deviance: 47.750  on 724  degrees of freedom
## Residual deviance: 43.307  on 722  degrees of freedom
## AIC: 22.519
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3971429     0.5955390 -0.15959256 50 5.5e-05 ***
##  2:      4.5      0.4247619     0.5658258 -0.10782010 75 2.1e-07 ***
##  3:      7.5      0.3733333     0.4935521 -0.09904220 75 4.4e-06 ***
##  4:     10.5      0.4838095     0.6203172 -0.10663262 75 1.3e-08 ***
##  5:     13.5      0.3923810     0.5287833 -0.11227501 75 5.8e-06 ***
##  6:     16.5      0.3523810     0.5469777 -0.15730356 75 5.6e-10 ***
##  7:     19.5      0.4361905     0.5547914 -0.08851555 75 8.8e-06 ***
##  8:     22.5      0.3314286     0.5052840 -0.14842435 75   1e-07 ***
##  9:     25.5      0.5085714     0.6171176 -0.10460691 75 1.5e-05 ***
## 10:     28.5      0.4247619     0.5694054 -0.12008650 75 1.3e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.15959256     24
##  2:  4.5 -0.10782010     24
##  3:  7.5 -0.09904220     24
##  4: 10.5 -0.10663262     24
##  5: 13.5 -0.11227501     24
##  6: 16.5 -0.15730356     24
##  7: 19.5 -0.08851555     24
##  8: 22.5 -0.14842435     24
##  9: 25.5 -0.10460691     24
## 10: 28.5 -0.12008650     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61001  -0.11261  -0.00817   0.12283   0.83868  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.00753    0.03454   0.218    0.828    
## timeNorm    -0.03366    0.04852  -0.694    0.488    
## obj.diff    -0.21449    0.03736  -5.741 2.21e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0583853)
## 
##     Null deviance: 20.394  on 318  degrees of freedom
## Residual deviance: 18.450  on 316  degrees of freedom
## AIC: 4.0881
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4090909     0.5804316 -0.14022422 22 0.00043 ***
##  2:      4.5      0.4978355     0.5474815 -0.06265206 33     0.077 .
##  3:      7.5      0.3766234     0.4539543 -0.07335885 33   0.0058 **
##  4:     10.5      0.3073593     0.4009189 -0.10218734 33   0.0015 **
##  5:     13.5      0.3073593     0.4562521 -0.14644435 33 8.3e-05 ***
##  6:     16.5      0.3203463     0.4583925 -0.10064086 33 0.00044 ***
##  7:     19.5      0.3116883     0.4472135 -0.09931286 33 2.5e-05 ***
##  8:     22.5      0.3809524     0.5140759 -0.13924753 33 0.00019 ***
##  9:     25.5      0.4545455     0.5170628 -0.06839461 33     0.035 *
## 10:     28.5      0.3246753     0.4704987 -0.12173515 33   6e-04 ***
##     time  error.diff shapes
##  1:  1.5 -0.14022422     24
##  2:  4.5 -0.06265206     16
##  3:  7.5 -0.07335885     24
##  4: 10.5 -0.10218734     24
##  5: 13.5 -0.14644435     24
##  6: 16.5 -0.10064086     24
##  7: 19.5 -0.09931286     24
##  8: 22.5 -0.13924753     24
##  9: 25.5 -0.06839461     24
## 10: 28.5 -0.12173515     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6740  -0.2383   0.1913   0.2315   0.4468  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.31220    0.08931   3.496 0.000547 ***
## timeNorm     0.04250    0.07455   0.570 0.569082    
## obj.diff    -0.66919    0.08659  -7.729 1.84e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1143287)
## 
##     Null deviance: 40.669  on 289  degrees of freedom
## Residual deviance: 32.812  on 287  degrees of freedom
## AIC: 199.05
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5428571     0.8728737 -0.29669053 20 9.5e-06 ***
##  2:      4.5      0.5809524     0.8803963 -0.25559896 30 5.6e-05 ***
##  3:      7.5      0.6714286     0.8577229 -0.13483882 30 0.00061 ***
##  4:     10.5      0.6095238     0.7604994 -0.13331250 30     0.031 *
##  5:     13.5      0.6238095     0.7595374 -0.13478768 30     0.13 :(
##  6:     16.5      0.6142857     0.7970813 -0.14997730 30   0.0011 **
##  7:     19.5      0.5619048     0.7279108 -0.11259797 30     0.096 .
##  8:     22.5      0.4809524     0.7183280 -0.19119972 30 0.00034 ***
##  9:     25.5      0.5666667     0.6705190 -0.08796937 30     0.35 :(
## 10:     28.5      0.6238095     0.6770076 -0.05763367 30     0.33 :(
##     time  error.diff shapes
##  1:  1.5 -0.29669053     24
##  2:  4.5 -0.25559896     24
##  3:  7.5 -0.13483882     24
##  4: 10.5 -0.13331250     24
##  5: 13.5 -0.13478768     16
##  6: 16.5 -0.14997730     24
##  7: 19.5 -0.11259797     16
##  8: 22.5 -0.19119972     24
##  9: 25.5 -0.08796937     16
## 10: 28.5 -0.05763367     16

## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61833  -0.30332   0.04882   0.26823   0.55284  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.32571    0.04760   6.843 2.48e-11 ***
## timeNorm    -0.07278    0.05505  -1.322    0.187    
## obj.diff    -0.64371    0.05013 -12.842  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1022827)
## 
##     Null deviance: 64.433  on 463  degrees of freedom
## Residual deviance: 47.152  on 461  degrees of freedom
## AIC: 263.84
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4955357     0.6208173 -0.102134051 32     0.054 .
##  2:      4.5      0.5416667     0.7465592 -0.219964008 48 1.4e-05 ***
##  3:      7.5      0.5238095     0.5671145 -0.057721683 48     0.21 :(
##  4:     10.5      0.5505952     0.5362800 -0.012090840 48     0.86 :(
##  5:     13.5      0.4910714     0.6014588 -0.102933948 48     0.058 .
##  6:     16.5      0.5714286     0.5871636 -0.024564724 48      0.7 :(
##  7:     19.5      0.3988095     0.5497972 -0.159240917 48   0.0053 **
##  8:     22.5      0.4880952     0.4926560 -0.007795557 48     0.89 :(
##  9:     25.5      0.4107143     0.4679547 -0.052574583 48     0.41 :(
## 10:     28.5      0.3720238     0.3847404 -0.033401276 48     0.52 :(
##     time   error.diff shapes
##  1:  1.5 -0.102134051     16
##  2:  4.5 -0.219964008     24
##  3:  7.5 -0.057721683     16
##  4: 10.5 -0.012090840     16
##  5: 13.5 -0.102933948     16
##  6: 16.5 -0.024564724     16
##  7: 19.5 -0.159240917     24
##  8: 22.5 -0.007795557     16
##  9: 25.5 -0.052574583     16
## 10: 28.5 -0.033401276     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71089  -0.17869  -0.08719   0.21121   0.71297  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10150    0.02962   3.427 0.000643 ***
## timeNorm     0.04189    0.03875   1.081 0.280065    
## obj.diff    -0.32835    0.03789  -8.665  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0796471)
## 
##     Null deviance: 67.040  on 753  degrees of freedom
## Residual deviance: 59.815  on 751  degrees of freedom
## AIC: 237.02
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.3571429     0.4760639 -0.111779680 52 0.0053 **
##  2:      4.5      0.4487179     0.4614922 -0.022793530 78   0.58 :(
##  3:      7.5      0.4139194     0.4300504 -0.025871902 78   0.57 :(
##  4:     10.5      0.4084249     0.4234581 -0.028539435 78   0.33 :(
##  5:     13.5      0.4047619     0.4001833  0.011787281 78   0.72 :(
##  6:     16.5      0.3443223     0.3337317 -0.003294444 78   0.96 :(
##  7:     19.5      0.3260073     0.2730373  0.026983247 78    0.5 :(
##  8:     22.5      0.2875458     0.2602781  0.022005152 78   0.54 :(
##  9:     25.5      0.2893773     0.2469083  0.020133496 78   0.63 :(
## 10:     28.5      0.3040293     0.2303798  0.036118595 78   0.47 :(
##     time   error.diff shapes
##  1:  1.5 -0.111779680     24
##  2:  4.5 -0.022793530     16
##  3:  7.5 -0.025871902     16
##  4: 10.5 -0.028539435     16
##  5: 13.5  0.011787281     16
##  6: 16.5 -0.003294444     16
##  7: 19.5  0.026983247     16
##  8: 22.5  0.022005152     16
##  9: 25.5  0.020133496     16
## 10: 28.5  0.036118595     16

Link between d(t-1) and error

## 
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.89243  -0.18839   0.03948   0.19600   0.68812  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.03898    0.01319   2.956  0.00316 ** 
## est.confidence.norm -0.19092    0.02330  -8.194 5.07e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0718048)
## 
##     Null deviance: 121.29  on 1623  degrees of freedom
## Residual deviance: 116.47  on 1622  degrees of freedom
## AIC: 335.41
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.87812  -0.07387   0.00530   0.08343   0.93023  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.113321   0.013285  -8.530   <2e-16 ***
## est.confidence.norm -0.002485   0.023014  -0.108    0.914    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06985935)
## 
##     Null deviance: 113.31  on 1623  degrees of freedom
## Residual deviance: 113.31  on 1622  degrees of freedom
## AIC: 290.81
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.93929  -0.17946  -0.01235   0.18914   0.90729  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         -0.00194    0.01798  -0.108  0.91409   
## est.confidence.norm -0.09542    0.03025  -3.154  0.00164 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1186153)
## 
##     Null deviance: 179.81  on 1507  degrees of freedom
## Residual deviance: 178.63  on 1506  degrees of freedom
## AIC: 1068.7
## 
## Number of Fisher Scoring iterations: 2

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTAll
## 
## REML criterion at convergence: 1138.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4577 -0.5580 -0.0296  0.5724  4.4769 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01651  0.1285  
##  Residual             0.07157  0.2675  
## Number of obs: 4756, groups:  IDjoueur, 58
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)           -0.04222    0.01899   81.00000  -2.224 0.028933 *  
## est.confidence.norm   -0.05669    0.01550 4745.00000  -3.657 0.000258 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.409

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTM
## 
## REML criterion at convergence: -217
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5313 -0.6734  0.0668  0.6616  2.9503 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.02759  0.1661  
##  Residual             0.04608  0.2147  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)           -0.02408    0.02741  102.60000  -0.879   0.3817  
## est.confidence.norm   -0.06189    0.03105 1418.90000  -1.994   0.0464 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.554

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTS
## 
## REML criterion at convergence: 110.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2365 -0.4898  0.0294  0.4751  4.4098 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01218  0.1104  
##  Residual             0.05823  0.2413  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          -0.14395    0.02208 144.50000  -6.519 1.11e-09 ***
## est.confidence.norm   0.05854    0.03049 961.70000   1.920   0.0552 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.693

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTL
## 
## REML criterion at convergence: 700.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3346 -0.5829 -0.0444  0.5885  3.5505 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.03712  0.1927  
##  Residual             0.08466  0.2910  
## Number of obs: 1508, groups:  IDjoueur, 52
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)           -0.08864    0.03414  102.60000  -2.596   0.0108 *
## est.confidence.norm    0.07232    0.03849 1325.70000   1.879   0.0604 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.583

CONFIDENCE SCALE APPROACH

VOIR AUTRE FICHIER